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Record W4387961666 · doi:10.21065/1925-7430.1.43

PREVALENCE OF DIABETIC COMPLICATIONS IN AN URBAN DISTRICT OF JHANG (PUNJAB) PAKISTAN

2011· article· en· W4387961666 on OpenAlexvenueno aff
Muhammad Shoaib Akhtar

Bibliographic record

VenueCanadian Journal of Applied Sciences · 2011
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiabetes mellitusObesitySocioeconomic statusRetinopathyNephropathyBlood sugarPopulationBlood pressureInternal medicineDiabetic retinopathyType 2 diabetesEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

The diabetic complications have become a world health problem. They prevail throughout the world but their percentages differ in different areas due to cultural influences. Therefore, we have determined prevalence of complications of diabetes in the rural population of district Jhang (Pakistan). For this purpose, a performa was developed and information was collected from two hundred and ten (210) diabetic patients about their age, sex, height, socioeconomic status, educational status, type of diabetes, duration of diabetes, age at diagnosis, blood pressure and blood sugar levels ( fasting and random) and the symptoms of diabetes. Mainly the complications were compared with different variables or risk factors like diet, type of diabetes, obesity, sugar levels and uncontrolled diet. Retinopathy, carbuncles, pregnancy and hypertension complications in both sexes had non-significant (P>0.05) relationship with controlled diet but significant (P<0.05) with uncontrolled diet. Nephropathy and neuropathy showed significant relationship with controlled diet and non-significant with uncontrolled diet. Retinopathy, neuropathy and gangrene in both sexes were highly significantly (P<0.001) related with both types of diabetes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.263
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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